Real-time diagnosis aid method and decision-support for medical diagnosis to a user of a medical system

ABSTRACT

A real-time diagnosis aid method including: (a) providing a decision-support system for autonomous medical diagnosis to a user of a medical system, the system comprising a display module and software modules embodied on a computer readable medium; (b) generating on the display module an ordered list of diagnostic clues to look at by the user according to the diagnostic clues already filled in as absent or present, the ordered list being based on the relevance of looking at respective diagnostic clues to quickly lead to a diagnosis by the user; (c) receiving from the user, information on presence or absence of one or more diagnosis clues listed in the ordered list; and (d) processing the received information to update the ordered list and provide on the display module an indication of the probability of each possible diagnosis.

TECHNICAL FIELD

The invention relates to a real-time diagnosis aid method and to adecision-support system for medical diagnosis to a user of a medicalsystem. The field of the invention relates more particularly to medicalimaging systems.

BACKGROUND

Building a decision support tool in medicine has been an objective sincethe beginning of the computer age. Many of these early works proposedrules-based expert system but in the 80's an important part of thecommunity investigated probabilistic reasoning based expert system.Probabilities and Bayesian methods were seen as a good way to handleuncertainty inherent to medical diagnosis.

The conditional independence assumption of symptoms given the diseasehas been extensively discussed as it is of crucial interest in terms ofcomputational tractability. Some researchers considered this assumptionharmless when others already proposed a maxent approach to face thisissue.

Nevertheless it seems that none of the works of that time has everconsidered the experts vs observations trade-off. These methods onlyhandle input data of probabilistic form. Namely they assume to have an apriori on marginals but also on some of the possible probabilitiescombinations and propose a maxent approach where these input data aretreated as constraints in the optimization process. This area ofresearch was very active in the 80s and then gradually disappeared,probably due to computational intractability of the proposed algorithms.Estimating a joint distribution from marginals is another very ancientproblem, not necessarily related to artificial intelligence, known inliterature as cell probabilities estimation problem in contingency tablewith fixed marginals.

Document WO2012/122196A2 discloses a decision-support system for medicaldiagnosis and treatment comprises software modules embodied on acomputer readable medium, and the software modules comprise aninput/output module and a question-answering module. The method receivespatient case information using the input/output module, and generates amedical diagnosis or treatment query based on the patient caseinformation and also generates a plurality of medical diagnosis ortreatment a plurality of medical diagnosis or treatment answers for thequery using the question-answering module. The method also calculatesnumerical values for multiple medical evidence dimensions from medicalevidence sources for each of the answers using the question-answeringmodule and also calculates a corresponding confidence value for each ofthe answers based on the numerical value of each evidence dimensionusing the question-answering module. The method further outputs themedical diagnosis or treatment answers, the corresponding confidencevalues, and the numerical values of each medical evidence dimension forone or more selected medical diagnosis or treatment answers using theinput/output module.

Document WO2016/097886A1 discloses a differential diagnosis apparatus,which is adapted for medical applications in order to determine anoptimal sequence of diagnostic tests for identifying a pathology byadopting diagnostic appropriateness criteria, comprising a firstupdatable database containing patients' data, a second relational database containing identification data of pathologies, symptoms, clinicalsigns, identification data of diagnostic tests, and data relating to theappropriateness parameters of said diagnostic tests for defining a listof diagnostic hypotheses (pathologies), means adapted to determine saidoptimal sequence of diagnostic tests for identifying a pathology, saidmeans comprising an inferential computation engine, which determine foreach diagnostic hypothesis (pathology), based on data contained in saidfirst and second databases, said optimal sequence of diagnostic testswith associated indices of appropriateness and probability that apatient is suffering from that pathology.

A usual difficulty, particularly in the medical field, is to transferthe expertise of expert users into a diagnosis assistance tool that canbe used by less expert users. These tools include tools based on the useof clues (abnormalities, measures or symptoms) to provide diagnoses. Themost common form is that where a list of clues is generated and the toolprovides a diagnosis or a diagnosis list with associated confidence.

A more advanced form of tools is the one providing indications on theclues to be checked according to the clues indicated as present orabsent. This optimizes the exam in real time. This principle can befound in the artificial intelligent software program Watson developed byIBM, which proposes a question to investigate according to the clues andindicates the consequences of different possible answers. There are alsotools prescribing the sequence of examinations to be performed. The twoprevious methods are based on their description of using a remotedatabase consulted to update the models. It turns out that there is aneed for a more flexible method where one only indicates an order ofpreference for the next exam to give flexibility to the physician whocan choose to be interested in the first one. index or any other indexwhich seems to him more relevant. A second need is to obtain a methodthat allows without being connected to the expert base to determine theordered list so that the device can be used offline. Another aim of theinvention is to build a diagnostic aid tool for the research of rarediseases by fetal ultrasound.

SUMMARY

This goal is achieved with a real-time diagnosis aid method comprisingsteps of:

-   -   providing a decision-support system for autonomous medical        diagnosis to a user of a medical system, said system comprising        a display module and software modules embodied on a computer        readable medium, said software modules comprising an        input/output module, a processing module and a        question-answering module,    -   generating on said display module an ordered list of diagnostic        clues to look at by said user according to the diagnostic clues        already filled in as absent or present, said ordered list being        based on the relevance of looking at respective diagnostic clues        to quickly lead to a diagnosis by said user,    -   receiving from said user, information on presence or absence of        one or more diagnosis clues listed in said ordered list,    -   processing said received information so as to update said        ordered list and provide on said display module an indication of        the probability of each possible diagnosis.

A software module provided for generating the ordered list canadvantageously comprise parameters which have been set by a neuronalnetwork trained by reinforcement learning techniques to minimize onaverage the remaining number of questions to lead to the diagnosis. Thisneural network can have a depth of at least four layers. The real-timediagnosis aid method according to the invention can further comprise astep for receiving from the user entry on other diagnosis clues relatedto the listed diagnosis clues by an ontology. Another diagnosis entrycan then be analyzed by a disease ontology module to process thereceived other diagnosis clues presence or absence, so as to relate themto the diagnosis clues listed in the ordered list in order to updatesaid ordered list and provide on said display module an indication ofthe probability of each possible diagnosis.

According to another aspect of the invention, there is proposed adecision-support system for medical diagnosis to a user of a medicalsystem, comprising:

-   -   a display module and software modules embodied on a computer        readable medium, said software modules comprising an        input/output module, a processing module and a        question-answering module,    -   a computer processor configured to generate on said display        module an ordered list of diagnostic clues to look at by said        user according to the diagnostic clues already filled in as        absent or present, said ordered list being based on the        relevance of looking at respective diagnostic clues to quickly        lead to a diagnosis by said user;        said computer processor being configured to receive from said        user information on presence or absence of one or more diagnosis        clues listed in said ordered list,        said computer processor being configured to process said        received information so as to update said ordered list and        provide on said display module an indication of the probability        of each possible diagnosis.

The decision support system according to the invention can furthercomprise a neural network provided for setting parameters within theordered list, said neural network being trained by reinforcementlearning techniques to minimize on average the remaining number ofquestions to lead to the diagnosis. The decision support systemaccording to the invention can further comprise a disease ontologymodule provided for analyzing free information entry received from theuser, so as to process said free information to update the ordered listand provide on the display module an indication of the probability ofeach possible diagnosis. According to yet another aspect of theinvention, there is proposed a computer program product, stored on anon-volatile computer-readable data-storage medium, comprisingcomputer-executable instructions to cause a computer to carry out amethod according to the invention.

The real-time diagnosis aid method according to the invention can beadvantageously implemented for prenatal diagnosis by ultrasound. In itspreferred instantiation, the real-time diagnosis-aid method according tothe invention is implemented in the form of a user interface presentinga list of ordered clues as well as a list of possible diagnosis with aconfidence level combined with an interface making it possible to informthe presence or the absence of ‘index.

The order of the clues as well as the level of confidence are providedby an autonomous module which takes as input the presence and absenceinformation already known index and determines these values withoutresorting to a database of cases or sickness. This is crucial forworking in situations where connectivity does not connect to a base orother computer, a strong constraint in a medical setting. This assumesof course that the existing parameters in the module have beenpreviously set. An example of such a module is given in the thesis of R.Besson and the associated article where this module consists of a neuralnetwork driven by reinforcement learning techniques to minimize onaverage the number of questions remaining to put. Other choices are ofcourse possible.

From the information given by this first part of the interface, the usercan decide at any time to stop or to inform the presence or absence ofclues that he could observe. One of the important aspects of theinvention is not to force the user to enter a given index but to givehim full freedom to choose the index. In particular, it allows to enterclues less precise than those listed. This freedom of choice is crucialto guarantee user autonomy and to integrate ontologies, especially inthe medical setting. Once this index is filled in the second part of theinterface, the first part is modified to take into account this newinformation.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic diagram featuring main sequences of a real-timediagnosis aid method according to the invention;

FIG. 2 features a simplified interface provided by the real-timediagnosis aid method according to the invention; and

FIGS. 3A-3E show examples of interfaces displayed in the real-timediagnosis aid method according to the invention, implemented fordiagnosing the prenatal rare disease and leading here to diagnosing theNoonan syndrome.

DETAILED DESCRIPTION

With reference to FIG. 1, a user of a medical system reports clues tothe real-time diagnosis aid system. These reported clues are thenprocessed by a calculator so as to update a list of ordered clues and todeliver diagnosis probabilities. A simplified display represented inFIG. 2 includes a list of clues ordered from a highest probability (Clue10 @ 10%)) to a lowest probability (Clue 6 @ 6%) related with a list ofdiagnostics ordered accord to decreasing probability, and two graphicbuttons provided for selecting the presence or the absence of a clue.

FIGS. 3A-3E give an overview of an interface for the decision supporttool according to the invention, for diagnosing the Noonan syndrome. Inthe beginning the “other” population is by far the most likely, this isthe case where the patient has no diseases referenced in the database:he can be healthy or affected by a disease that does not belong to thebase.

Each symptom of the initial database has been mapped to the HPO (HumanPhenotype Ontology) database. It has been possible to extract theunderlying tree structure linking the different HPO codes. For each HPOcode (a given description of a symptom), all its descendants (moreprecise description of such symptom) are known, as such as all itsascendants (less precise description of such symptom).

As previously recited, the aim of the real-time diagnosis aid methodaccording the invention is to give more freedom to the users whendescribing the symptoms they observed, providing them the possibility todescribe the symptoms at different level of granularity. Then, insteadof providing answers at a given level of granularity (the initial listof symptoms), one could allow the physician to choose any of the HPOcode. It involves an explosion in the number of possible symptoms: theformer list of symptoms references some 200 signs when the HPO ontologyhas around 1300 signs.

Theoretically each patient could be unique if its symptoms are describedto a sufficient level of accuracy. Nevertheless, when one lists thetypical symptoms of a disease, it is possible to generalize and findpatterns in patient profiles. The symptoms combination distributions aremodelized with the initial database (the one with 200 symptoms)preserving the ability of generalization the algorithms. Symptoms tocheck are proposed at the level of granularity of the initial databasebut the real-time diagnosis aid method allows the user to give answersat a different level of granularity (any HPO code can be chosen). Byproceeding in this way, a less rigid decision support is providedwithout computation explosion since all computation are done at theinitial level of granularity. For such an objective, a functiontranslating the received imprecise information (the HPO code) intousable information,—for example presence/absence of symptoms at a givengranularity level-, is required. Such a function involves deterministicand stochastic rules.

Deterministic Rules

The function associating the usable information to each HPO code impliessome automatic (deterministic) rules:

-   -   If a positive answer is received for a given HPO code, all its        ascendants should be given a positive answer too.    -   If a negative answer is received for a given HPO code, all its        descendants should be given a negative answer too.

In practice, during the medical examination all the information givenabout the HPO codes selected by the user are stored. In order to computethe probability of each disease, it is required to check, for each HPOcode and each disease, if this HPO code is in the list of the symptomsrelated to this disease. If not, it is required to check whereasascendants or descendants of this HPO code are in this given list ofsymptoms. According to the two above-cited deterministic rules, if theHPO code was declared to be present, it is required to check ifascendants are in the list; if it was declared to be absent, it isrequired to check the descendants.

If the HPO code verifies all the following assertions, it can beconsidered as non typical and processed in consequence:

-   -   the HPO code is not in the list of symptoms related to the        disease.    -   It is present (respectively absent) and its        ascendants/descendants (respectively descendants) are not in the        list.

In practice all these operations can be pre-computed in order tominimize the computation time required. An easy way to do it is to storein a list indexed on symptoms, the position the symptom has (or one ofits ascendant/descendant has) in the list of typical symptoms of eachdisease is stored in a list indexed on symptoms.

Stochastic Rules

Assuming for example that the presence of an “abnormal heart morphology”has been observed but that the disease of interest for a user has in itslist of typical symptoms the “Hypoplasia of the right ventricle”.Decision stochastic rules can be implemented for this issue. Whenreceiving the information of the presence of a HPO code, it requires todetermine which of its descendants are in the list of symptoms of thefirst database (the one which is used to build the environment model).All these symptoms have a known probability of apparition (given whathas been observed) and it is possible to compute them.

With L as a list of symptoms for which there is no descendant in theinitial database or which are absent. Then let assume that a user hasobserved the presence of a symptom which potential descendants are B₁⁽¹⁾, B₂ ⁽¹⁾, B₃ ⁽¹⁾ and B₄ ⁽¹⁾ and the presence of a second symptomwhich potential descendants are B₁ ⁽²⁾ and B₂ ⁽²⁾.

There are then 4×2=8 possible combinations. Indeed, without anyadditional assumption the number of possible combinations could belarge. But it is assumed that for each imprecise answer there is onlyone descendant which is present at a time. The function first computes∀i, j, D:

P[B_(i) ⁽¹⁾,B_(j) ⁽²⁾,L]D].

It is just the matter of searching for each D which are the typicalsymptoms in the list and B_(i) ⁽¹⁾, B_(j) ⁽²⁾, L use the deterministicrules if necessary.

It is then possible to compute:

${{P\left\lbrack {B_{i}^{(1)},\left. B_{j}^{(2)} \middle| L \right.} \right\rbrack} \propto {P\left\lbrack {B_{i}^{(1)},B_{j}^{(2)},L} \right\rbrack}} = {\sum\limits_{D}{{P\left\lbrack {B_{i}^{(1)},B_{j}^{(2)},\left. L \middle| D \right.} \right\rbrack}{P\lbrack D\rbrack}}}$

The probability of each disease is displayed by:

${{P\left\lbrack {\left. D \middle| \hat{B} \right.,L} \right\rbrack} = {{{P\left\lbrack {\left. D \middle| L \right.,{\bigcup_{i,j}\left( {B_{i}^{(1)}\bigcap B_{j}^{(2)}} \right)}} \right\rbrack} \propto {{P\left\lbrack {L,\left. {\bigcup_{i,j}\left( {B_{i}^{(1)}\bigcap B_{j}^{(2)}} \right)} \middle| D \right.} \right\rbrack}{P\lbrack D\rbrack}}} = {\sum\limits_{i,j}{{P\left\lbrack {L,B_{i}^{(1)},\left. B_{j}^{(2)} \middle| D \right.} \right\rbrack}{{P\lbrack D\rbrack}.}}}}}\;$

with {tilde over (B)} referred as the fuzzy state associated to the 8possible states B_(i) ⁽¹⁾ and B_(j) ⁽¹⁾

It can also be useful to optimize the subtasks which start from symptomswhich do not have any descendants in the database. By this way, it isnot necessary to use the stochastic rule while training the neuralnetworks.

Furthermore, when receiving an imprecise answer, the algorithm shouldask all the time to the physician user if he could provide a moreprecise answer. If not, a computation has to be performed in real timeduring the examination. This computation should not last more than asecond, otherwise it can be considered that the provided information wasnot precise enough and can be overlooked.

To avoid using the stochastic rules while training the neural network,it is also needed to remove all the action which has descendants andreplace them by their descendants in the data base. It is also possibleto compute quickly the probabilities without making the assumption ofconditional independence, by relaxing the model of dependence betweensymptoms. Assume a two-stage deep ontology with a deeper stage forspecific symptoms description and a more vague level for organs.

For a disease with K₁ cardiac typical symptoms (C₁, . . . , C_(K1)) andK₂ renal typical symptoms (R₁, . . . , R_(K2)):

$R = \left\{ \begin{matrix}1 & {{if}\mspace{14mu} {there}\mspace{14mu} {is}\mspace{14mu} {at}\mspace{14mu} {least}\mspace{14mu} {one}\mspace{14mu} {renal}\mspace{14mu} {abnormalities}} \\0 & {{otherwise}.}\end{matrix} \right.$

Then it is assumed that symptoms from distinct organs are conditionallyindependent given which organs have abnormalities, so the followingdecomposition is obtained:

P[C₁…  , C_(K₁), R₁, …  , R_(K₂)|D] = P[C₁, …  , C_(K₁)|C, D] × P[R₁, …  , R_(K₂)|R, D] × P[C, R|D].

It should be noted that even if the possibility has been lost to storedependence between precise symptoms from different organs (C_(i) andR_(j)), a model of dependence can be kept at the higher level inontology: dependence between organs abnormalities (C and R). Instead ofcomputing and storing all symptoms combinations, one can just store thesymptoms combinations inside organs and organs combinations.

The probability of symptom combinations is computed with the assumptionto present at least one symptom (which was yet an assumption before).The organs abnormality combinations P [R, C|D] are then computed. Whenmarginals P [R|D] or P [C|D] are not known, they can be processed asmissing values or try to approximate them using marginals of the lowerlevel, temporarily making some kind of conditional independenceassumption.

Each symptom combination can be easily computed using the law of totalprobability, for example the following decomposition can be obtained:

${P\left\lbrack {{\overset{\_}{R}}_{1},\left. C_{1} \middle| D \right.} \right\rbrack} = {{{P\left\lbrack {\left. {\overset{\_}{R}}_{1} \middle| \overset{\_}{R} \right.,D} \right\rbrack} \times {P\left\lbrack {\left. C_{1} \middle| C \right.,D} \right\rbrack} \times {P\left\lbrack {\overset{\_}{R},\left. C \middle| D \right.} \right\rbrack}} + {{P\left\lbrack {\left. {\overset{\_}{R}}_{1} \middle| \overset{\_}{R} \right.,D} \right\rbrack} \times {P\left\lbrack {\left. C_{1} \middle| C \right.,D} \right\rbrack} \times {P\left\lbrack {R,\left. C \middle| D \right.} \right\rbrack}}}$

where P [R ₁|R,D]=1 and all the other probabilities have been storedmaking these kinds of computations very cheap.

This approach is in fact well adapted for several diseases whichmanifested them-selves in combinations of symptoms coming from specificorgans. For example the VACTERL syndrome is a rare genetic diseasesdefined by a combination of at least three abnormalities from threedistinct organs among vertebral anomalies, anorectal malformation,cardiovascular anomalies, tracheoesophageal fistula, esophageal atresia,renal and/or radial anomalies and limb defects.

Then it is possible to cope with any symptom combination distributioneven when the number of related symptoms to a disease is high. In such acase it needs to find ascendants common to several of these symptoms(which is always possible by definition) that will organize the symptomsin groups (the organs in the example of VACTERL syndrome). Theconditional independence assumption between symptoms given theascendants is then made. Finally, it is possible to integrate theontological information while remaining in the probabilistic setting.This results in a less rigid decision support tool without computationexplosion.

A decision-support system according to the invention should be a gooddecision support tool for a rare disease diagnostic task. It takes intoaccount the need, in medicine, to achieve a high level of certainty whenpossessing a diagnostic. The aim is to minimize the average number ofmedical tests to be performed before reaching this level of certainty.Several reinforcement learning algorithms have been used in a highdimensional and reward-sparse setting. To do this the initial task hasbeen broken into several sub-tasks, with a policy for each sub-tasks. Anappropriate use of the intersections between the sub-tasks cansignificantly accelerate the learning procedure.

Embodiment of a Processing Software Module for the Real-Time DiagnosisAid Method

A software module which processes the list of presence/absence of cluesand delivers the ordered list of relevant next symptoms to check willnow be described. This software module includes a neuronal network whichis trained by reinforcement learning techniques to minimize on averagethe remaining number of questions to lead to the diagnosis. In anapproximated value-based approach, the neural network is learned toapproximate the optimal Q-values of the different states, i.e. theremaining average number of questions to lead to the diagnosis.

The main drawback of such an algorithm—and by extension of the deepreinforcement learning algorithms in general—, is its huge need forcomputing resources. A very large number of simulations are requiredbefore obtaining a good approximation of the optimal Q-values. This isparticularly true in a high-dimensional setting, i.e. when the statespace dimension is high. It's a challenge both in terms of computingtime and in terms of learning stability (the returns suffer from a highvariance). To scale up on such problems, the state space has been brokeninto a partition and leverage already solved sub-tasks as bootstrappingmethods. A concrete example of this algorithm is given below:

Algorithm 1 DQN-MC with Bootstrapping on Already Solved Sub-Tasks.

Start with low dimensional tasks.

 for i such that the task

 has not been yet optimized do   if | 

_(i)| ≤ 30 then    while the budget for the optimization of this task   has not been reached do     Play 100 games (e-greedy) from the start

 to a terminal state.     Integrate all the obtained transitions to theReplay-Memory     Throws part of the Replay-Memory away     (the oldesttransitions of the replay)     Sample 1/20 of the Replay-Memory    Perform a gradient ascent step (back     propagation algorithm) onthe sample   end while  end if end for  Continue with higher-dimensiontasks.  while there are still tasks to be optimized do   Choose theeasiest task to optimize: the one with the   highest proportion   ofalready solved sub-tasks (weighted by their probability to be faced)  while the budget for the optimization of this task has not   beenreached do    Play 100 games (e-greedy) from the start

 to a terminal    state (condition (j))    or to a state that was yetencountered in an already solved task    (condition) (jj))    if westopped a game because of condition (jj) then    Bootstrap i.e. use thenet work of the sub-tasks to predict    the average number of questionto reach a terminal state   end if   Integrate all the obtainedtransitions to the Replay-Memory   Throws part of the Replay-Memory away(the oldest transitions   of the replay)   Sample 1/20 of theReplay-Memory   Perform a gradient ascent step (backpropagationalgorithm)  end while end while

The goal of reaching on average as fast as possible a state where adiagnosis can be made starting with the information of the presence of aparticular symptom, corresponds to a sub-stack. Indeed, from theinformation of the presence of a particular symptom S_(i), the methodwill focus on the symptoms related with it, namely the symptoms whichshare common diseases, to then retain only the symptoms that remainrelevant to check.

If

_(i)=(S_(i) ₁ , S_(i) _(k) ) is a set of symptoms related with thesymptom S_(i), with

_(i) being small enough (for example

_(i)<11), the optimal policy can be learned by a simple Q-learninglookup table algorithm. For intermediate dimension problems (for example11<

_(i)<31), the DQN-MC algorithm can be used performs with efficiency.DQN-MC is just the classical DQN algorithm where the classic TD updatehas been replaced by a MC update.

For small and intermediate dimension problems (

i<31), 100 games are played, and all the transitions annotated with thereward they received (i.e. the number of questions that have beennecessary to reach a terminal state during the concerned game) areincorporated to a replay memory. Then, one twentieth of thesetransitions from this memory is sampled in order to perform a gradientascent with a back-propagation algorithm.

For high-dimensional problems (

i>30), using directly the DQN algorithm would be time-consuming. An easyway to accelerate the learning phase of these big networks is to makeuse of the smaller networks previously trained. Indeed, if B_(i) is asymptom for which

_(i) is high, there must have some B_(j)∈

_(i) such as

_(j) is small enough and therefore such as the Q-values of the optimalpolicy for j have been yet computed or at least approached.

The Q-networks are learnt one after the other and there is therefore amore preferable order than others for optimizing these deep networks. Ateach step, it is decided to optimize the Q-networks which has thehighest rate of sub-problems already solved (where each sub-task isweighted by its probability to be faced).

On small subproblems, the DQN-MC algorithm, when optimized by aREINFORCE algorithm, has quasi-optimal performances in comparison with aconventional decision-tree algorithm, and the related energy-basedpolicy, where the log probability of selecting an action is proportionalto an energy function, appears to clearly outperform a classic Breimanalgorithm and all the more so as the dimension increases: the averagenumber of questions to ask may be divided by two in some cases. On smallsubproblems where it is possible to compute the optimal policy by adynamic programing algorithm, this energy-based policy appears to bevery close to the optimal policy.

Of course, the present invention is not limited to the embodiment thathas been described as a best mode and many other embodiments can beconsidered without departing from the scope of the invention. Moreover,many medical systems other than an ultrasound imaging system canimplement a real-time diagnosis-aid method according to the invention.

REFERENCES

Note: The documents below are incorporated herein by Reference.

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What is claimed is:
 1. A real-time diagnosis aid method comprising:providing a decision-support system for autonomous medical diagnosis toa user of a medical system, said system comprising a display module andsoftware modules embodied on a computer readable medium, said softwaremodules comprising an input/output module, a processing module and aquestion-answering module; generating on said display module an orderedlist of diagnostic clues to look at by said user according to thediagnostic clues already filled in as absent or present, said orderedlist being based on the relevance of looking at respective diagnosticclues to quickly lead to a diagnosis by said user; receiving from saiduser, information on presence or absence of one or more diagnosis clueslisted in said ordered list; and processing said received information soas to update said ordered list and provide on said display module anindication of the probability of each possible diagnosis.
 2. Thereal-time diagnosis aid method according to claim 1, wherein a softwaremodule generates said ordered list comprises parameters which have beenset by a neuronal network trained by reinforcement learning techniquesto minimize on average the remaining number of questions to lead to saiddiagnosis.
 3. The real-time diagnosis aid method according to claim 2,wherein said neural network has a depth of at least four layers.
 4. Thereal-time diagnosis aid method according to claim 1, further comprisingreceiving from said user entry on other diagnosis clues related to saidlisted diagnosis clues by an ontology.
 5. The real-time diagnosis aidmethod according to claim 4, wherein another diagnosis entry is thenanalyzed by a disease ontology module to process said received otherdiagnosis clues presence or absence, so as to relate them to saiddiagnosis clues listed in said ordered list.
 6. A decision-supportsystem for medical diagnosis to a user of a medical system, comprising:a display module and software modules embodied on a computer readablemedium, said software modules comprising an input/output module, aprocessing module and a question-answering module; a computer processorconfigured to generate on said display module an ordered list ofdiagnostic clues to look at by said user according to the diagnosticclues already filled in as absent or present, said ordered list beingbased on the relevance of looking at respective diagnostic clues toquickly lead to a diagnosis by said user; said computer processor beingconfigured to receive from said user information on presence or absenceof one or more diagnosis clues listed in said ordered list; and saidcomputer processor being configured to process said received informationso as to update said ordered list and provide on said display module anindication of the probability of each possible diagnosis.
 7. Thedecision support system according to claim 6, further comprising aneural network setting parameters within said ordered list, said neuralnetwork being trained by reinforcement learning techniques to minimizeon average the remaining number of questions to lead to said diagnosis.8. The decision support system according to claim 6, further comprisinga disease ontology module analyzing free information entry received fromsaid user, so as to process said free information to update said orderedlist and provide on said display module an indication of saidprobability of each possible diagnosis.
 9. A computer program product,stored on a non-volatile computer-readable data-storage medium,comprising computer-executable instructions comprising: providing adecision-support system for autonomous medical diagnosis to a user of amedical system, said system comprising a display module and softwaremodules embodied on a computer readable medium, said software modulescomprising an input/output module, a processing module and aquestion-answering module; generating on said display module an orderedlist of diagnostic clues to look at by said user according to thediagnostic clues already filled in as absent or present, said orderedlist being based on the relevance of looking at respective diagnosticclues to lead to a diagnosis by said user; receiving from said user,information on presence or absence of one or more diagnosis clues listedin said ordered list; and processing said received information so as toupdate said ordered list and provide on said display module anindication of the probability of each possible diagnosis.
 10. Animplementation of a real-time diagnosis aid method according to claim 1,for prenatal diagnosis by ultrasound.